arXiv Machine Learning

VIBE: Vector Index Benchmark for Embeddings

arXiv:2505. 17810v2 Announce Type: replace Abstract: Approximate nearest neighbor (ANN) search is a performance-critical component of many machine learning pipelines, and rigorous benchmarking is essential for assessing the performance of vector indexes for ANN search.

arXiv AI

Jun 4

No More K-means: Single-Stage Sparse Coding for Efficient Multi-Vector Retrieval

arXiv:2605. 30120v3 Announce Type: replace-cross Abstract: Multi-vector retrieval (MVR) models, exemplified by ColBERT, have established new benchmarks in retrieval accuracy by preserving fine-grained token-level interactions.

By Lixuan Guo, Yifei Wang, Tiansheng Wen, Aosong Feng, Stefanie Jegelka, Chenyu You

rag efficiency benchmarks

arXiv Machine Learning

Aug 3

FORGE: Frame Orthogonality in Relevance Geometry for Long-Form Video Understanding

arXiv:2607. 25266v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have enabled long-form video understanding at a scale that was not previously possible.

By Ghazal Kaviani, Ghassan AlRegib

llms rag nlp multimodal

arXiv AI

Jul 21

LookME: Lookup-Based Multimodal Embeddings for Layer Injection in Vision-Language Models

arXiv:2607. 16305v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have achieved strong progress in multimodal understanding.

By Zeyu Xu, Xingzhong Hou, Pengkai Guo, Siling Lin, Xiao Xu, Menghua Zhai, Haoyu Chen, Yunke Zhang, Fei Huang

llms rag multimodal benchmarks

arXiv AI

Jun 17

Non-negative Elastic Net Decoding for Information Retrieval

arXiv:2606. 17910v1 Announce Type: cross Abstract: Dense retrieval has become the dominant paradigm in information retrieval, in which each document is scored against a query by the inner product of their vector embeddings, and the top-$k$ documents by score are retrieved for this query.

By Koki Okajima, Yasutoshi Ida, Tsukasa Yoshida, Yasuaki Nakamura

rag benchmarks

arXiv AI

Jul 7

Panorama: Fast-Track Nearest Neighbors

arXiv:2510. 00566v4 Announce Type: replace-cross Abstract: Approximate Nearest-Neighbor Search (ANNS) pipelines for high-dimensional neural embeddings spend the bulk of their query time in candidate verification, making it the primary bottleneck in the search process.

By Vansh Ramani, Alexis Schlomer, Akash Nayar, Sayan Ranu, Jignesh M. Patel, Panagiotis Karras

rag efficiency benchmarks

arXiv Machine Learning

Jun 16

MVEB: Massive Video Embedding Benchmark

arXiv:2606. 14958v1 Announce Type: cross Abstract: We introduce the Massive Video Embedding Benchmark (MVEB), a 23-task benchmark for video embeddings spanning classification, zero-shot classification, clustering, pair classification, retrieval, and video-centric question answering.

By Adnan El Assadi, Roman Solomatin, Isaac Chung, Chenghao Xiao, Deep Shah, Manan Dey, Shriya Sudhakar, Zacharie Bugaud, Wissam Siblini, Ayush Sunil Munot, Yashwanth Devavarapu, Rakshitha Ireddi, Michelle Yang, M\'arton Kardos, Niklas Muennighoff, Kenneth Enevoldsen

rag nlp multimodal benchmarks

arXiv AI

6d ago

Can Frontier LLMs Match Natively Multimodal Embeddings? A Comparison on Hard-Negative Text-to-Image Retrieval

arXiv:2608. 11343v1 Announce Type: new Abstract: Multimodal retrieval and classification across different types of media, spanning text, images,video and audio, has traditionally relied on dual-encoder models that align visual and textual representations through contrastive learning.

By Archan Dutta, Vyanktesh Kanungo

llms rag diffusion multimodal

arXiv Machine Learning

Jul 14

Retrieval with Multiple Query Vectors through Anomalous Pattern Detection

arXiv:2605. 01965v2 Announce Type: replace Abstract: A classical vector retrieval problem typically considers a \emph{single} query embedding vector as input and retrieves the most similar embedding vectors from a vector database.

By Allassan Tchangmena A Nken, Baimam Boukar Jean Jacques, Miriam Rateike, Celia Cintas, Skyler Speakman

rag

arXiv AI

Jun 16

MMLongEmbed: Benchmarking Multimodal Embedding Models in Long-Context Scenarios

arXiv:2606. 14747v1 Announce Type: cross Abstract: Recent advancements have significantly expanded the theoretical context windows of Multimodal Embedding Models (MEMs).

By Haitian Wang, Ruoxi Sun, Quantong Qiu, Juntao Li, Junhui Li, Hua Chen, Jinxiong Chang, Min Zhang

rag multimodal benchmarks

arXiv Machine Learning

Aug 5

Geo-Embed: Towards Unified Multimodal Embeddings for Urban Understanding

arXiv:2608. 03826v1 Announce Type: cross Abstract: Geospatial and urban applications increasingly require models to compare heterogeneous evidence across street-view imagery, remote-sensing observations, text descriptions, region proposals, and temporal change cues.

By Jiapeng Li, Yong Li, Junjie Zhou, Fan Zhang, Yu Liu

rag nlp multimodal benchmarks

arXiv AI

Jun 30

Multimodal Representation Alignment for Cross-modal Information Retrieval

arXiv:2506. 08774v2 Announce Type: replace-cross Abstract: Different machine learning models can represent the same underlying concept in different ways.

By Fan Xu, Luis A. Leiva

llms rag multimodal benchmarks safety

arXiv AI

Jun 3

$\mathbb{R}^{2k}$ is Theoretically Large Enough for Embedding-based Top-$k$ Retrieval

arXiv:2601. 20844v3 Announce Type: replace-cross Abstract: This paper studies the Minimal Embeddable Dimension (MED): the least dimension in which there exists a configuration of $m$ object vectors so that every subset of size at most $k$ is exactly retrieved by score comparison.

By Zihao Wang, Hang Yin, Lihui Liu, Hanghang Tong, Yangqiu Song, Ginny Wong, Simon See

llms rag

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